AI agent governance platform
Govern AI agents with explicit controls: RBAC permissioned actions, approval prerequisites, audit trails with evidence, and safe reruns for reliability across real workflows.
Governance primitives
Permissioned actions (RBAC)
Agents act under permissions. Least privilege stays enforceable as teams and workflows expand.
- ✓Scoped permissions
- ✓Fail-closed authorization
- ✓Audit logged denials
Approvals and policies
Approval policies route decisions to owners with auditable, deterministic behavior.
- ✓Policy routing
- ✓Escalations
- ✓Approval history
Audit trails with evidence
Evidence links allow reconstructing decisions quickly during audits and incidents.
- ✓Evidence linkage
- ✓Searchable history
- ✓Explainable outputs
Operational safety
Safe reruns and retries
Reruns preserve audit trails and avoid duplicate side effects with idempotency patterns.
- ✓Idempotency patterns
- ✓Deterministic reruns
- ✓Clear failure reasons
Confidence and flags
Uncertainty is explicit and routed to review queues. High-impact steps fail closed.
- ✓Confidence/flags
- ✓Review queues
- ✓Fail-closed behavior
Tenant-safe execution
Isolation and controlled boundaries are prerequisites for trustworthy multi-tenant automation.
- ✓Tenant scoping
- ✓Controlled boundaries
- ✓Auditable isolation
Related
Follow the runtime and governance surface into specific product workflows.
FAQ
Clear answers for teams evaluating governance and runtime design.
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